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Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Theses
As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …
Transcriptomics Of Learning, Pablo Iturralde
Transcriptomics Of Learning, Pablo Iturralde
Theses
Learning is a basic and important component of behavior yet we have very little empirical information about the interaction between mechanisms of learning and evolution. In our work, we are testing hypotheses about the neurogenetic mechanisms through which animal learning abilities evolve. We are able to test this directly by using experimentally evolved populations of flies, which differ in learning ability. These populations were previously evolved within the lab by creating worlds with different patterns of change following theoretically predicted effects on which enhanced learning will evolve. How has evolution acted to modulate genes and gene expression in the brain …
Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane
Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane
Theses
Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …